Eye tracking system and method to detect the dominant eye

ABSTRACT

The invention relates to an eye tracking system ( 10 ) and a method for operating an eye tracking system ( 10 ) for determining if one of a left and a right eye of a user is dominant, wherein at least one image of the left and the right eye of the user is captured (S 30 ), based on the at least one image and according to a predefined accuracy function a left accuracy score for the left eye (S 34   a ) and a right accuracy score for the right eye (S 34   c ) is determined and it is determined if one of the left and the right eye of the user is dominant in dependency of at least the left and the right accuracy score (S 36 ). Thereby user-specific properties relating to his eyes can be provided and considered when performing eye tracking so that the robustness and accuracy of eye tracking can be enhanced.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/526,005, filed on May 11, 2017, which is the national phase entry ofIntl. Patent App. No. PCT/EP2015/073185, filed on Oct. 7, 2015, whichclaims priority to European Patent App. No. 14193277.2, filed on Nov.14, 2014, which are all incorporated by reference in their entireties.

Known eye tracking systems and eye tracking devices usually captureimages of the eyes of a user and therefrom determine a gaze direction, apoint of regard or other eye features. For this purpose, the position,orientation or other properties of the eyes of a user can be determined.To be able to take into account user-specific properties, e.g. the shiftbetween the optical axis and the visual axis of the eye, a calibrationprocedure can be performed for calibrating the eye tracking system.During such a calibration procedure, usually calibration points, alsocalled stimulus points, are shown on a display in a timely manner andthe user has to fixate these calibration points with his eyes.Meanwhile, the eye tracking system captures images of the eyes of theuser, calculates the gaze or point of regard, compares the calculatedpoints of regard to the positions of the shown calibration points anddetermines a map that minimizes the difference between the calculatedpoints of regard and the shown calibration points. So, by taking intoaccount user-specific properties of the eyes the eye tracking qualitycan be enhanced.

The object of the present invention is to provide a method for operatingan eye tracking system and an eye tracking system, which can providemore user-specific properties relating to his eyes, especially which canenhance the robustness or accuracy of eye tracking.

This object is solved by a method for operating an eye tracking devicefor determining if one of a left and a right eye of a user is dominantand an eye tracking system according to the independent claims.Advantageous embodiments of the invention are presented in the dependentclaims.

According to the method for operating an eye tracking system fordetermining if one of a left and a right eye of a user is dominant inaccordance with the invention, at least one image of the left and theright eye of the user is captured and based on the at least one imageand according to a predefined accuracy function a left accuracy scorefor the left eye and a right accuracy score for the right eye aredetermined. Furthermore, it is determined if one of the left and theright eye of the user is dominant in dependency on at least the left andthe right accuracy score.

By determining the left and the right accuracy score, especially wherethese accuracy scores give information about the tracking accuracy withrespect to the left and the right eye respectively and separately, itcan easily be determined whether there is a dominant eye or not. Knowingthe dominant eye can enormously enhance the tracking quality. The reasonfor this is that usually data relating to both eyes are used for eyetracking and therefore are weighted equally. If there is a dominant oron the other hand a weak eye, this can result in distorting the eyetracking results tremendously, for example with respect to thecalculated point of regard. Advantageously, according to the inventionit is possible to determine if there is a dominant eye and to use thisresult, for example, for eye tracking purposes but also for diagnosticpurposes. Detecting the dominant eye of a person can be used to diagnoseeye vergence pathologies such as Strabismus and Amblyopia. Furthermore,the point-of-regard-accuracy of the eye tracking system can be improvedby automatically detecting if a strong eye dominance or presence of eyevergence pathologies results in an eye being unable to accurately fixateobjects. Hence, for example the eye tracking data associated with anon-dominant eye can be discarded or weighted less than the eye trackingdata associated with the dominant eye.

The respective accuracy scores are determined on the basis of image dataof the at least one image, wherein an image of the left eye and an imageof the right eye can be captured and processed separately, e.g. theimages of the left and the right eye can be captured by two separatecameras or image sensors, or on the other hand it is also possible tocapture one common image of the left and the right eye, which isprocessed for deriving image data relating to the left eye fordetermining the left accuracy score and for deriving image data relatingto the right eye for determining the right accuracy score. It's alsopreferred to capture more than one single image, especially plenty ofimages, e.g. hundreds of images, on the basis of which the left andright accuracy score is determined, because then the accuracy andreliability of the outcome of the determination of the dominant eye issignificantly higher.

The accuracy function according to which the respective accuracy scoresare calculated can be a predefined mathematical formula, an algorithm, alook-up-table, and so on, which has image data of the respective eye ordata of the respective eye calculated on the basis of the image as inputand gives the respective accuracy score as output. Furthermore, themethod according to the invention can be performed, especiallyautomatically, during a calibration procedure and/or validationprocedure for validating calibration results. Also at least part of themethod can be performed after such procedures using results of thecalibration and/or validation procedure. For example, errors, like aresidual error for the left eye, the right eye and a binocular residualerror, calculated during a calibration procedure and/or validationprocedure can serve as the respective accuracy score and the functionsand/or algorithms for calculating these errors could then constitute theaccuracy function.

In an advantageous embodiment of the invention, a signal is providedand/or outputted in dependency on the left and right accuracy score,especially a signal indicating the identity of the dominant eye. Thissignal can, for example, be used to output the information about thedominant eye to the user or another person, e.g. on a screen, fordiagnosis purposes. Also, the signal can be used to indicate whethercaptured data relating to both eyes or only captured data relating tothe left or the right eye should be used for eye tracking. Soadvantageously, the result of the determination whether there is adominant eye can enhance the functionalities of diagnostic devices andthe capabilities and accuracies of eye tracking devices.

Furthermore, based on the at least one image and according to thepredefined accuracy function, a binocular accuracy score for the leftand the right eye can be determined, and it is determined if one of theleft and the right eye of the user is dominant in dependency on theleft, right and binocular accuracy score. By taking into account thebinocular accuracy score, which defines e.g. an accuracy score for thebinocular gaze or binocular point, of regard, that is related to theleft and the right eye, a decision whether there is a dominant eye canbe made more reliably. Moreover, the above-mentioned signal can beprovided and outputted in dependency on the left, right and binocularaccuracy score. Based on the signal, a channel, namely a left channel, aright channel or a binocular channel, can then be chosen for eyetracking, especially wherein the left channel provides eye data relatingto the left eye, e.g. the gaze direction of the left eye or the point ofregard of the left eye, the right channel similarly provides data withrespect to the right eye and the binocular channel provides data withrespect to both eyes, e.g. the binocular gaze or binocular point ofregard. Beneficially, based on the best accuracy score the best of thesethree channels can be chosen for eye tracking. Thereby, the eye trackingaccuracy and robustness can be optimized.

In general the left gaze direction can be calculated on the basis ofimage data of the at least one image relating to the left eye. The leftpoint of regard is determined as the intersection of the determined gazevector of the left eye with a predefined plane. This plane can be e.g.the display or a screen of a monitor, especially a remote monitor, orthe display or a screen of a head mounted display device. In case of 3Dcapable display devices or in case there is a lens or other opticalsystem in between the display device and the user's eyes the plane canalso be a virtual image plane. The coordinates of this plane is known tothe eye tracking system and therefore the intersection of the gazevector and the plane can be calculated as the point of regard. The sameapplies for the right gaze direction and the right point of regard. Thebinocular gaze direction is the averaged gaze direction of the left andthe right eye. The binocular point of regard can be calculated inseveral ways, for example the binocular point of regard can becalculated as the Intersection of the binocular gaze vector with thepredefined plane or as the middle of the right and left point of regardon the predefined plane or as an projection of the intersection point ofthe left and the right, gaze vectors onto the predefined plane, e.g. inthe direction of the binocular gaze direction.

Moreover, the left accuracy score is a measure for a deviation of apoint of regard of the left eye from a left set point of regard, and theright accuracy score is a measure for a deviation of a point of regardof the right eye from a right set point of regard and the, especiallyoptional, binocular accuracy score is a measure for a deviation of abinocular point of regard of the left and the right eye from a binocularset point of regard. Or in other words, these deviations can becalculated and the left, right and optionally also the binocularaccuracy score can be determined or calculated in dependency of therespective deviations, especially wherein other influencing parameterscan be considered when determining the respective accuracy scores aswell. To determine these deviations, a procedure similar to acalibration procedure can be used. Thereby, calibration points, alsocalled stimulus points, which correspond to the set points of regard,can be shown on a display and a left, right and binocular point ofregard and their respective deviations or differences to the set pointsof regard can be determined. Then, these deviations can be used tocalculate the accuracy score for the left and the right eye and thebinocular accuracy score for both eyes. Besides these deviations of thepoints of regard from set points of regard, further influencingvariables can be considered when determining the respective accuracyscore, like the number of validly tracked points of regard. Thecalculation of the respective accuracy scores in dependency on theseinfluencing variables or other variables can be defined by thepredefined accuracy function. As this procedure for determining therespective accuracy scores is similar to a calibration procedure and/orvalidation procedure it can be advantageously be embedded in acalibration and/or validation procedure, which makes it particularlytime efficient and minimises the computing effort and computing time.

Furthermore, in general the left set point of regard, the right setpoint of regard and the binocular point of regard, which can be e.g.shown on a display, can be—but need not—be different in their positions.For example, first a number of left set points of regard can be shownone after the other on a display and for each shown left set point ofregard the corresponding left point of regard of the user can bedetermined, especially the position of the left point of regard of theuser on the display, and therefrom the deviations of each determinedleft point of regard from the position of the respective left set pointof regard can be calculated. After that, a number of right set points ofregard can be shown and the right points of regard of the user and therespective deviations can be determined analogously. After that, anumber of binocular set points of regard can be shown one after theother and the binocular points of regard of the user for each binocularset point of regard and the respective deviations can be determinedanalogously as well. But to shorten this procedure, it is veryadvantageous if instead of separate or different set points for the lefteye, right eye and both eyes, shared set points are used for the left,right and both eyes. So, for example one set point can be shown, whichthen constitutes the left set point of regard, the right set point ofregard and optionally the binocular set point of regard at the sametime, meaning the right point of regard of the user, the left point ofregard of the user and optionally the binocular point of regard of theuser is determined while the user is looking at that single set point,and the deviation of the left point of regard from the shown set pointis determined, the deviation of the right point of regard from the shownset point is determined and optionally the deviation of the binocularpoint of regard from the set point is determined. This is then repeatedfor a number of set points shown one after the other. Consequently, muchtime can be saved when using shared set points of regard for determiningthe left, right and optionally binocular point of regard and therespective deviations, wherein this time saving effect is the morebeneficial the more set points are shown and by this more comfort can beachieved for the user additionally.

Therefore, it s a preferred and advantageous embodiment of the inventionthat the left, right and binocular set points of regard are identicaland preferably are provided by showing a finite sequence of points, i.e.stimulus points, on a display device of the eye tracking system that theuser is required to fixate and/or by showing a smooth and/or continuousmotion of a point or other stimulus image on the display device that theuser is required to follow with both eyes. For example, a single setpoint of regard can be shown on a display or a screen as the left, rightand binocular set point of regard and then the left point of regard, theright point of regard and the binocular point of regard can bedetermined by the at least one image captured during the user isfixating the shown set point of regard and the respective accuracy scorecan be determined from the respective deviations of the determinedpoints of regard from the set point of regard. This can be done forplenty of set points of regard shown in a temporal sequence or even in asmooth motion so that the respective accuracy scores can be determinedmore precisely.

According to a preferred embodiment of the invention the determinationif one of the left and the right eye of the user is dominant isperformed during a calibration procedure of the eye tracking systemand/or during a validation procedure for validating results of thecalibration procedure. As usually a calibration procedure is performedanyway, the results of this procedure can advantageously be used fordetermining the respective accuracy scores and thereby the dominant eye.Additionally, also the results of a validation procedure can be used forthis purpose, which makes the results even more reliable. For example,first a calibration procedure can be performed, during which somecalibration points as the set points of regard are shown on a display ina timely manner. While these points are shown, the eye tracking systemcaptures several images of both eyes of the user and defines theparameters of a calibration map that maps the determined points ofregard to such calibrated points of regard that minimize the deviationfrom the shown calibration points. Afterwards, a validation procedurecan be performed, during which second set points of regard can be shownon a screen or display the user has to look at and further images arecaptured while these points are shown. Based on the captured furtherimages and based on the calibration map, the parameters of which weredetermined in the calibration procedure, again the points of regard ofthe left, right eye and the binocular point of regard are determinedwith respect to each second set point and again the error of the left,right and binocular point of regard with respect to each second setpoint can be calculated. Then, these errors, namely that for the lefteye, for the right eye and the binocular error, can be used to determinethe respective accuracy scores.

So in general, during the calibration procedure of the eye trackingsystem several first points of regard of the user are calculated andcompared to corresponding first set points of regard, wherein on thebasis of this comparison calibration parameters of the eye trackingsystem are set so that the calculated first points of regard are mappedto corresponding calibrated first points of regard. This is particularlydone with respect to the left and the right eye each, i.e. a calibrationmap is defined for the left eye and the right eye separately, so thatinformation about differences between the left and the right eyeproperties are not lost.

Furthermore, during the validation procedure, several second points ofregard of the user are calculated and compared to corresponding secondset points of regard. By means of the validation procedure, the resultsof the calibration procedure can be validated. In other words, byperforming the validation procedure it can be determined whether thecalibration was successful or not. So, advantageously, this validationprocedure can be used for two purposes, namely for validating theresults of the calibration procedure and on the other hand the resultsor measurements performed during the validation procedure can be usedfor calculating the respective accuracy scores and thereby determiningthe dominant eye.

So, preferably, on the basis of the deviation of the calibrated firstpoints of regard from the first set points of regard an error of thecalibration procedure for the left eye, the right eye and for both eyesis calculated and/or on the basis of the deviation of the second pointsof regard from the second set points of regard an error of thevalidation procedure for the left eye, the right eye and for both eyesis calculated. Advantageously, either only the results of thecalibration procedure can be used for determining the dominant eye,which allows for a very quick performance, or the results of thevalidation procedure can be used, which allows for much higher accuracyand robustness.

In a further embodiment of the invention, during the calibrationprocedure an optimal subset of calibration points is automaticallydetected by excluding outliers according to a predefined criterion,preferably by evaluating all the subsets of the calibration points or byfinding a randomized consensus. Sometimes it might happen that the userwho is required to look at the shown set points might be distracted orotherwise fails to look at the set points so that it is veryadvantageous to exclude such outliers as they would falsify the result.For example, if five set points are shown, all three-point, four-pointand five-point subsets can be listed and analyzed with regard to therespective determined points of regard of the user and then the bestsubset can be chosen. The same can be done with just a random sample ofsubsets instead of taking all possible subsets into account, which isadvantageous if lots set points are shown.

Additionally, the left, right and binocular accuracy score can be eachdetermined In dependency on whether a left point of regard, right pointof regard and binocular point of regard, respectively, can be calculatedon the basis of the at least one image and/or the error of thecalibration procedure and/or validation procedure for the left eye, theright eye and both eyes, respectively. Sometimes it is not possible todetermine the left, right or binocular point of regard, e.g. because oneor both eyes cannot be detected in the captured image. Being unable todetect one or both eyes is also a measure for the eye tracking quality.If, for example, for five shown set points of regard the left point ofregard can only be determined twice, whereas the right point of regardcan be determined five times, the tracking quality for the right eye canbe assumed to be much better than the tracking quality for the left eye.So, advantageously, also the information regarding the number of validcalibration points used in model fitting for defining the calibrationmap or the information regarding the number of well tracked validationpoints can be considered when calculating the respective accuracyscores. As already mentioned, above all the error of the calibrationprocedure and/or validation procedure for the left, the right and botheyes is a very good measure for the tracking quality, so it is veryadvantageous to consider this information as well when calculating therespective accuracy scores.

In a further embodiment of the invention, eye tracking is performed,wherein a present point of regard is calculated in dependency on asignal, especially the signal indicating the dominant eye. So, if duringeye tracking, i.e. after the calibration and/or validation procedureshave been finished, the present point of regard of a user is repeatedlydetermined. This present point of regard can be advantageouslydetermined in dependency on the information about the dominant eye. If,for example, the signal indicates that the left eye is the dominant eye,the present point of regard of the user can be calculated on the basisof only the data concerning the left eye to get better tracking results,or In other words the present point of regard can be calculated similarto the left point of regard described above, e.g. using only thedetermined left eye gaze vector, if the signal indicates that both eyesare equally dominant, then the present point of regard can be calculatedas binocular present point of regard on the basis of the informationconcerning the left as well as the right eye. Therefore, eye trackingcan be performed much more precisely and more robustly, whichfurthermore enhances also all the applications controlled by eyetracking, for example when the eye tracking system is used forcontrolling a mouse with a determined point of regard or for activatingan element in a graphical user interface or also for selecting a focuspoint corresponding to the focus point of the user, and the like.

So preferably, the present point of regard is calculated with respect tothe left eye if the left eye was determined as being dominant, thepresent point of regard is calculated with respect to the right eye ifthe right eye was determined as being dominant, and the present point ofregard is calculated with respect to both eyes if none of the left andright eye was determined as being dominant.

In a further advantageous embodiment of the invention, the present pointof regard is calculated as a weighted average between left eye data andright eye data, wherein weights of the freighted average are a functionof the determined left accuracy score and right accuracy score, so thatthe right eye data is more heavily influencing the weighted average thanthe left eye data if the right accuracy score is higher than the leftaccuracy score, and the left eye data is more heavily influencing theweighted average than the right eye data if the left accuracy score ishigher than the right accuracy score. The calculation of the presentpoint of regard as a weighted average gives much more flexibility withregard to the optimization of the accuracy and even better results canbe achieved. Especially because the binocular point of regard is muchmore robust than just calculating the point of regard only based on lefteye data or only based on right eye data, this makes it possible thatthe data concerning both eyes can be used and still differences withregard to the accuracy of the respective eye can be taken into account.

The invention further relates to an eye tracking system for determiningif one of a left and a right eye of a user is dominant, wherein the eyetracking system is configured to capture at least one image of the leftand the right eye of the user, based on the at least one image andaccording to a predefined accuracy function to determine a left accuracyscore for the left eye and a right accuracy score for the right eye andto determine if one of the left and the right eye of the user isdominant in dependency on at least the left and the right accuracyscore.

The preferred embodiments and advantages thereof described with regardto the method for operating an eye tracking system according to theinvention correspondingly apply to the eye tracking system according tothe invention. In particular, the described steps of the method foroperating an eye tracking system and its embodiments according to theinvention constitute further embodiments of the eye tracking systemaccording to the invention. For example the eye tracking system cancomprise a capturing unit, one or more cameras and/or image sensors, forcapturing the at least one image and a processing unit for processingthe captured images and determining the respective accuracy scoresaccording to the predefined accuracy function and to determine if one ofthe left and the right eye of the user Is dominant. Furthermore, the eyetracking system can comprise a display device for showing the set pointsof regard. Moreover, the eye tracking device can comprise one or morelight sources for illuminating the eyes and to produce reflections onthe eyes which are used to determine features of the eyes like gazedirection and/or point of regard and/or the position and/or orientationof the respective eye.

In the following, advantageous embodiments of the present invention aredescribed in more detail with reference to the accompanying drawings.

They show in:

FIG. 1 a schematic illustration of a method for operating an eyetracking system according to an embodiment of the invention;

FIG. 2 a schematic view of five calibration points shown on a display ofan eye tracking system together with a determined point of regard forthe left and the right eye each;

FIG. 3a a schematic view of four validation points shown on a display ofan eye tracking system during a validation procedure together with adetermined point of regard of the left and the right eye each accordingto an embodiment of the invention;

FIG. 3b a schematic view of four validation points shown on a display ofthe eye tracking system during a validation procedure together with adetermined point of regard of the left and the right eye each accordingto an embodiment of the invention;

FIG. 4 a schematic illustration of the method for operating an eyetracking system, wherein a channel of the eye tracking system is chosenin dependency on the accuracy score of each channel according to anembodiment of the invention; and

FIG. 5 a schematic view of an eye tracking system for determining thedominant eye of a user according to an embodiment of the invention.

In the following description of preferred embodiments for the previouslyused terms left set point of regard, right set point of regard andbinocular set point of regard the term stimulus point is used as asynonym and furthermore, depending on whether these stimulus points areshown during a calibration procedure or validation procedure, thesestimulus point are also called calibration points or validation points,respectively.

FIG. 1 shows a schematic view of a method for operating an eye trackingsystem 10 (compare FIG. 5) to determine the dominant eye of a useraccording to an embodiment of the invention. The method starts in stepS10, where an input is received for starting a calibration procedure ofthe eye tracking system 10. This input can be a user input but alsosomething different like a general starting signal. When this input isreceived, the eye tracking system 10 starts the calibration procedure.This calibration procedure can be performed as a standard calibration instep S12 a or also optionally as a smart calibration In step S12 b.Whether the standard calibration in step S12 a or the smart calibrationIn step S12 b is performed can also be indicated by the input signal instep S10.

If the user starts the standard calibration process, in step S12 astimulus points are shown at n predefined static positions on thestimulus plane and the user is supposed to look at these points. Thestimulus plane can be a screen or an image plane of a display device ofthe eye tracking system, wherein the eye tracking system can beimplemented as a remote eye tracker or also as a head mounted eyetracking device comprising head mounted displays. Thereby, the eyetracking system can also be configured to provide three dimensionalimages so that the stimulus plane not necessarily has to coincide withthe physical screen plane of a display but can also be a virtual imageplane produced by a 3D display or by stereoscopic displays. The positionand orientation of this stimulus plane is known to the eye trackingsystem 10.

When the user looks at these stimulus points, the eye tracking systemcaptures images of both eyes of the user and thereby, for example, cancalculate the 3D position and/or orientation of each of the eyes and thegaze vectors of each of the eyes, wherein the point of regard of theleft eye then is defined as the intersection of the gaze vector of theleft eye with the stimulus plane, the right point of regard is definedas the intersection of the right eye gaze vector with the stimulus planeand the binocular point of regard can be defined as the intersection ofa binocular gaze vector with the stimulus plane or also can be simplydefined as the positional average of the left point of regard and theright point of regard on the stimulus plane. The stimulus points furtherare shown each at a time. The ground truth point of regards for these nstimulus points, which can also be denoted as set points of regard, arepriorly known as p^(G) _(i=1˜n,) which are normally represented by 2Dvectors. From the eye tracking software accordingly n gaze samples g^(L)_(i=1˜n) from the left gaze channel, and n gaze samples g^(R) _(i=1˜n)from the right gaze channel are obtained. Each gaze sample g_(i)includes all the eye and/or gaze information relevant to the i^(th)calibration point.

The calibration has to produce a gaze correction model M: g→M(g) whichmaps the raw gaze samples to the calibrated ones. Especially there isproduced a gaze correction model for the left eye M^(L) and for theright eye M^(R) each. The gaze correction model will ideally be able tocorrect all gaze samples from this user and the following gaze tracking.

Let the (priorly known) function that computes or extracts the point ofregard from the gaze sample g be P: g→p=P(g). The gaze correction forthe left eye M^(L) is then optimized to minimize the difference betweenthe calibrated point of regard p^(L)=P(M^(L)(g^(L))) and the groundtruth p^(G), as

$\begin{matrix}{{\underset{M^{L}}{\arg\;\min}{\sum\limits_{i = 1}^{n}{{p_{i}^{L} - p_{i}^{G}}}^{2}}} = {\underset{M^{L}}{\arg\;\min}{\sum\limits_{i = 1}^{n}{{{P\left( {M\left( g^{L} \right)} \right)} - p_{i}^{G}}}^{2}}}} & (1)\end{matrix}$

The residual error of the left channel on the i^(th) calibration pointis computed asr _(i) ^(L) =∥P(M ^(L)(g ₂ ^(L)))−p _(i) ^(G)∥.  (2)

The calibration is always done for the two monocular channelsseparately. Especially the same applies for the right channel in thesame way and equations (1) and (2) also apply for the right channel ifthe index “L” is substituted by “R”. Normally, the point of regard ofthe binocular gaze channel is created by averaging the left and rightchannels, as

$\begin{matrix}{p^{B} - \frac{p^{L} + p^{R}}{2} - {\frac{{P\left( {M^{L}\left( g^{L} \right)} \right)} + {P\left( {M^{R}\left( g^{L} \right)} \right)}}{2}.}} & (3)\end{matrix}$

Therefore, the residual error for the binocular channel can be computedasr _(i) ^(B) =∥p _(i) ^(B) −p _(i) ^(G)∥.  (4)

Instead of static stimulus points, the calibration can also be done byshowing a continuously moving stimulus point. The path of the movingpoint is fully specified by a path model p^(G), and this is the groundtruth point of regard in the calibration. The gaze correction for theleft eye M^(L) is optimized to minimize the difference between the pathof the calibrated point of regard and the ground truth path by doing adynamic time warping:

$\begin{matrix}{{{\underset{M^{L}}{\arg\;\min}{\int_{t}\mspace{11mu}{{{{p^{L}(t)} - {\min\limits_{\substack{\Delta\; t \\ {- T} < {\Delta\; t} < T}}{p^{G}\left( {t + {\Delta\; t}} \right)}}}}^{2}{dt}}}} = {\underset{M^{L}}{\arg\;\min}{\int_{t}\mspace{11mu}{{{{P\left( {M\left( {g^{L}(t)} \right)} \right)} - {\min\limits_{\substack{\Delta\; t \\ {- T} < {\Delta\; t} < T}}{p^{G}\left( {t + {\Delta\; t}} \right)}}}}^{2}{dt}}}}},} & (5)\end{matrix}$

where T controls the maximal time window for matching.

The overall residual error of the left channel is exactly what we triedto minimize above

$\begin{matrix}{r^{L} = {\int_{t}\mspace{11mu}{{{{p^{L}(t)} - {\min\limits_{\substack{\Delta\; t \\ {- T} < {\Delta\; t} < T}}{p^{G}\left( {t + {\Delta\; t}} \right)}}}}^{2}{{dt}.}}}} & (6)\end{matrix}$

Again the same applies for the right channel. The residual error for thebinocular channel can be computed in a similar way after

$\begin{matrix}{{r^{B} = {\int_{t}\mspace{11mu}{{{{p^{B}(t)} - {\min\limits_{\substack{\Delta\; t \\ {- T} < {\Delta\; t} < T}}{p^{G}\left( {t + {\Delta\; t}} \right)}}}}^{2}{dt}}}},{{{with}\mspace{14mu} p^{B}} = {\frac{p^{L} + p^{R}}{2}.}}} & (7)\end{matrix}$

So during the calibration process in step S12 a the correction modelsM^(L) and M^(R) for the left and the right eye are calculated as well asoptionally the errors r^(L), r^(R) and r^(B) for the left channel, theright channel and the binocular channel, respectively. These results ofthe calibration process can then be outputted or provided in step S14for further use by the eye tracking system, for example for a subsequenteye tracking and/or for a subsequent validation procedure.

These results can also directly be used for determining the dominant eyein step S22 in form of a self validation, which is explained later.

After the calibration procedure in step S12 a is finished, the user canmake an input in step S16 for activating a validation procedure, whichthen is performed in step S18 using the results of the calibrationprovided in step S14. Instead of a user input in Step S16 any otheractivation signal can be provided for activating the validationprocedure. The validation procedure can also be started automaticallyafter the calibration procedure is finished and step S16 can be omitted.

During the validation procedure, again stimulus points are shown on thestimulus plane but preferably at positions that are different from thepositions of the calibration points.

While these stimulus points are shown, images of the eyes of the userare captured by the eye tracking system and eye features of each eye,like gaze vectors, are determined. Thereby, one again gets raw gazesamples g^(L) _(j=1˜m) for the left eye and g^(R) _(j)=_(1˜m) for theright eye for each of the ground truth points of regard p^(G) _(j=1˜m),which are the positions of the stimulus points. Then the validationerror for each channel, namely the error r^(R) for the right channel theerror r^(L) for the left channel and the error r^(B) for the binocularchannel, is computed by applying the calibrated gaze correction model M,especially M^(L) and M^(R):

$\begin{matrix}{{r_{i}^{L} = {{{P\left( {M^{L}\left( g_{j}^{L} \right)} \right)} - p_{j}^{G}}}},} & (9) \\{{r_{i}^{R} = {{{P\left( {M^{R}\left( g_{j}^{R} \right)} \right)} - p_{j}^{G}}}},} & (10) \\{r_{i}^{B} = {{{\frac{{P\left( {M^{L}\left( g_{j}^{L} \right)} \right)} + {P\left( {M^{R}\left( g_{j}^{L} \right)} \right)}}{2} - p_{j}^{G}}}.}} & (11)\end{matrix}$

Instead of showing single stimulus validation points, again a validationstimulus point can be shown in a continuously moving manner as describedwith regard to the calibration procedure.

Furthermore, instead of the standard calibration described in step S12a, a smart calibration can be performed in step S12 b. The reason forthis is that due the distraction of the user or other problems, not alldetected fixations in the calibrations procedure are good for thecalibration purpose. Therefore, in the smart calibration a screeningprocedure can be implemented to detect and remove the “bad” calibrationpoints. This feature provides higher calibration robustness and is alsorelevant to the calibration quality.

The smart calibration is similar to the standard calibration in thatfirst of all multiple calibration stimulus points are shown on astimulus plane, one after the other, and for each stimulus point thegaze and/or point of regard of the left eye, the right eye and thebinocular gaze and/or point of regard is determined. The difference tothe standard calibration is that not necessarily all of the stimuluspoints and the corresponding eye properties, like gaze and determinedpoints of regard, are used for providing the correction model M.

Considering a set of n calibration points, the smart calibrationprocedure could be as follows:

First of all, all possible and interested subsets are enumerated. Forexample, when we have five points, all three-point, four-point andfive-point subsets will be listed and evaluated in the following steps.

In the next step, for each calibration set three measures areconsidered: The number of calibration points, the quality (residual) ofthe calibration, and the deviation of the gaze correction fromtranslation and scaling. There is also a prior knowledge basedpreference towards the subset consisting of more points. For a subsetunder testing, the calibration is done only using the points in the set,and then the corresponding residual error on the calibration set iscomputed. Empirically, bad calibration points often require a large“gaze correction” to minimize the residual. Therefore, a large deviationfrom a model that applies only translation and scaling is a sign of badcalibration points. All theses three measures will be combined into ascore by a weighted sum. In the next step, among ail the subsets thebest one is selected based on their score. If the subset that consistsof all points wins over all the other subsets, the calibration continueswith all the n points. If this is not the case, the gaze correctionmodel from the best subset will be used. The calibration points that arenot in the selected subset will be labelled as “not used for badquality”.

This proposed approach is a reliable approach even for a small set ofcalibration points, e.g. five points. For a calibration with a large setof calibration points, e.g. more than eight, a more general sampleconsensus approach, for example RANSAC (Random Sample Consensus), can beused to detect the best subset.

So if finally the best subset is determined, the correction model M andresidual errors as described for the standard calibration procedure instep S12 a can be calculated, especially with regard to the chosensubset, and provided in step S14. If then the standard validationprocedure is performed afterwards In step S18, the results of the smartcalibration procedure are used similarly to those described above withregard to the standard calibration.

After this validation procedure in step S18, the validation results areprovided in step S20 for determining the dominant eye of the user instep S22. Especially these validation results are the residual errorsprovided by (9), (10) and (11) or the respective overall residual errorsin case of a smooth pursuit. Moreover, also the number of validlytracked points regarding each of the eyes can be provided as validationresult.

With regard to determining the dominant eye, the basic idea is toevaluate the quality of each gaze channel based on the calibrationand/or validation quality, and the best channel (binocular, left, right)can be chosen for eye tracking. If a validation procedure had beenperformed in step S18, the monocular validation error is the mostnatural quality measure for each monocular channel. The validationquality can also include the information about the number of welltracked validation points. So, based on the computed validation errorsfor each channel according to equations (9), (10) and (11), and furthertaking Into account the number of well tracked validation points foreach channel, an accuracy score can be determined for each of thechannels. This accuracy score is the higher, the smaller the respectivevalidation error and the higher the number of well tracked validationpoints is. Optionally, a “bonus”, e.g. in form of a predefined numericalvalue, can be added to the accuracy score of the binocular channel sothat in doubt the binocular gaze is usually more robust than themonocular gazes. Additionally, it could also be considered whether thenumbers of valid gaze samples in the validation procedure is veryunbalanced between the left and right channels, so that also informationabout the number of well tracked validation points can be used forcalculating the respective accuracy scores. For example, it may bechecked whether one channel has more than two times the number oftracked points compared with the other channel. If the left channel onlygets two points tracked in the validation and the right channel getsfive points, the system will switch to using the right channel for itsbetter robustness. So, to determine the dominant eye, the system willcompare the quality of the three channels based on the correspondingaccuracy scores and pick the best one. The general rule is to use thebest channel, while the validation quality is the most natural andreliable quality measure.

So, if the left channel is determined as the best channel, the left eyeis determined to be the dominant eye. If the left and the right channelhave equal or similar accuracy scores or the accuracy score of thebinocular channel is the best one, then it is determined that both eyesare equally dominant or, in other words, that there is no dominant eye.Based on these results in step S22 an output signal in step S24 can beprovided indicating the dominant eye. The signal then can be used foroutputting the results of this determination on a screen or it can beused for a subsequent eye tracking by using the best channel.

In another embodiment, the validation procedure does not have to beperformed necessarily. Optionally, the calibration results in step S14can be used directly for determining the dominant eye in step S22 as akind of self validation. So, this self validation allows for a gazechannel selection without additional validation. Thereby, the leftand/or right and/or binocular gaze channel quality is determined basedon the calibration quality. Therefore, the calibration results,especially the residual errors according to (2), for the left channel,the corresponding one for the right channel and the binocular residualerror according to (4) or the respective overall residual errorsaccording to (6) and (7) can be used to calculate the correspondingaccuracy scores, which define the calibration quality for each channel.Also, the number of usable calibration points can be considered whencalculating the respective accuracy scores. So, additionally, when thetwo channels have a different number of usable calibration points,wherein the reason could be smart calibration, loss of eye tracking, andso on, the channel with the fewer calibration points will be given avery low quality or accuracy score, for its calibration is much lessreliable, even when its residual is smaller.

To sum up, the self validation is done on the calibration set, whereinthe preferred solution is to use the calibration quality, i.e. the meanresidual, as the validation quality. Another option would be the“leave-one-out cross-validation”. Also, the calibration quality caninclude the information regarding the number of valid calibration pointsused in model fitting, since more calibration points generally result ina more accurate gaze correction. For providing the accuracy scores foreach of the channels considering all or some of the above-mentionedquality influencing parameters, an accuracy function can be provided,wherein for example all these parameters can be summed up withcorresponding weights. So, based on the so calculated accuracy scoresfor each channel, the dominant eye can be determined in the same way asdescribed with regard to the results of the validation procedure andagain a signal indicating the dominant eye can be outputted in step S24.

FIG. 2 shows a schematic view of five stimulus points shown in astimulus plane during the calibration procedure together with thecalibrated points of regard for the left and right eye each. Thecalibration points or stimulus points are denoted in FIG. 2 as p_(i)^(G,C) for i=1, 2, 3, 4, 5. Furthermore, the determined and calibratedpoints of regard for the left eye are denoted by P(M^(L)(g_(i) ^(L,C)))for i=1, 2, 3, 4, 5 and the determined and calibrated points of regardfor the right eye are denoted by P(M^(R)(g_(i) ^(R,C))). In FIG. 2 fivestimulus points p_(i) ^(G,C) are shown for illustrative purposes all inone image, but during the calibration procedure these stimulus pointsp_(i) ^(G,C) are shown one at a time. Also, the shown calibrated pointsof regard P(M^(L)(g_(i) ^(L,C))), P(M^(R)(g_(i) ^(R,C))) are just forillustrative purposes and not shown during the calibration procedure.Actually, FIG. 2 can be seen as the result of the calibration procedurewith the shown stimulus points p_(i) ^(G,C) and the correspondingdetermined and calibrated points of regard P(M^(L)(g_(i) ^(L,C))),P(M^(R)(g_(i) ^(R,C))).

As can be seen, for each stimulus point p_(i) ^(G,C) the point of regardP(M^(L)(g_(i) ^(L,C))), P(M^(R)(g_(i) ^(R,C))) of the left and the righteye can be determined. In this case, the number of validly trackedpoints of regard P(M^(L)(g_(i) ^(L,C))), P(M^(R)(g_(i) ^(R,C))) for theleft and the right eye is five each. The residual error r^(L) for theleft channel can be determined as 0.5° in x-direction and 0.7° iny-direction, wherein the residual error r^(R) for the right eye or rightchannel can be determined as 0.1° in x-direction and 0.5° iny-direction. These results could directly be used as self validation fordetermining the dominant eye, wherein in this example no dominant eyewould be detected as the accuracies for the left and the right channelare quite similar.

Optionally, after this calibration procedure, a validation procedure canbe performed using the calibration model M determined on the basis ofthe calibration procedure. Two examples of a validation procedure,especially their results, are shown in FIG. 3a and FIG. 3 b.

In FIG. 3a four validation points are shown, which are denoted by p_(i)^(G,V1) with i=1, 2, 3, 4, and which are different in their positionsfrom the calibration points p_(i) ^(G,C). Furthermore, the points ofregard relating to each of the validation points with respect to theright and the left eye each, which are determined during the validationprocedure, are shown, too. The points of regard for the left eye aredenoted by P(M^(L)(g_(i) ^(L,V1))), with 1=1, 2, 3, 4, and the points ofregard for the right eye are denoted by P(M^(R)(g_(i) ^(R,V1))). In thiscase, the average residual error r^(L) for the left channel is 0.3° inthe x-direction and 0.6° in the y-direction and the average residualr^(R) error for the right channel is 0.2° in the x-direction and 0.3° inthe y-direction. As these points of regard P(M^(L)(g_(i) ^(L,V1))),P(M^(R)(g_(i) ^(R,V1))) of the left, and the right eye have comparablevalidation qualities in this example, the binocular channel is chosenfor eye tracking or other purposes. Based on these validation results,it can be determined that none of the left and the right eye is adominant eye.

In contrast thereto, FIG. 3b shows another example with differentresults of the validation procedure. Again, four validation pointsdenoted by p_(i) ^(G,V2) with i=1, 2, 3, 4, are shown, which are againdifferent in their positions from the calibration points p_(i) ^(G,C).Also, the points of regard with respect to the right and the left eyeare shown, which are denoted by P(M^(L)(g_(i) ^(L,V2))) for the left eyeand P(M^(R)(g_(i) ^(R,V2))) for the right eye, with i=1, 2, 3, 4. Theaverage residual error r^(R) for the right channel in this case is 0.7°for the x-direction and 0.6° for the y-direction, whereas for the lefteye no useful points could be determined. In this case, as can be seen,the validation quality for the right eye is much better than for theleft eye and tracking can be switched to monocular tracking with thebetter eye, in this case the right eye, using the right channel.

So, in general, if the difference between the validation qualities,which can be provided by calculating the corresponding accuracy scores,especially based on the respective errors, is above a predefinedthreshold, the better channel can be chosen for better robustness.

FIG. 4 shows a schematic illustration of a method for operating an eyetracking system according to another embodiment of the invention. Instep S30 the eye tracking system captures images of both eyes of a user.This can be done by using one or more cameras. On the basis of thecaptured images, data relating to the left eye are derived in step S32a, with regard to the right eye in step S32 c, and with regard to botheyes in step S32 b. These data can, for example, be eye properties suchas the position of the eyeball, the position of the pupil, the positionsof eventually detected glints, the gaze direction and/or points ofregard. Based on these data in each channel, namely for the left, eye,the right eye and both eyes, a respective accuracy score is calculatedfor the left eye in step S34 a, for the right eye in step S34 c and forboth eyes in step S34 b. This accuracy score is a measure for thetracking quality in each channel. Based on these accuracy scores, instep S38 it is determined whether one of the left or the right eye isdominant. For example, if the left accuracy score and the right accuracyscore are equal or their difference is below a predefined threshold, itis determined that none of the left and the right eye is dominant. Ifthe left accuracy score is higher than the right accuracy score andoptionally additionally the difference between the left and rightaccuracy score exceeds a predefined threshold, the left eye isdetermined as being dominant, especially except the binocular accuracyscore is still higher than the left and the right one. The same appliesfor the right eye, namely if the right accuracy score is higher than theleft accuracy score and optionally additionally the difference betweenthe left and right accuracy score exceeds a predefined threshold, theright eye is determined as being dominant, especially except thebinocular accuracy score is still higher than the left and the rightone. In all other cases it would be determined that there is no dominanteye.

So, if for example it is determined in step S36 that the left eye is thedominant eye, a signal is provided in step S38 a indicating the left eyeas the dominant eye and eye tracking is performed using the leftchannel, if otherwise it would have been determined that the right eyeis the dominant eye, a signal is outputted in step S38 c indicating theright eye as the dominant eye and eye tracking is performed using theright channel. If in step S36 it would have been determined that no eyeis dominant, a signal is outputted in step S38 b indicating that no eyeis dominant and both eyes or rather the binocular channel is used foreye tracking.

Alternatively, if one of the eyes is determined as dominant eye by thesignal, it also would be possible to use the binocular gaze for eyetracking but wherein the binocular gaze is calculated on the basis ofthe left and right eye data, wherein the eye data of the dominant eyeare weighted more heavily, e.g. the ratio of the weights could be orcorrespond to the ratio of the accuracy scores.

FIG. 5 shows a schematic view of an eye tracking system 10 according toan embodiment of the invention. The eye tracking system 10 comprises aprocessing or control unit 12, a display unit 14 and a capturing unit16. Optionally, the eye tracking system 10 can comprise an illuminationunit 18. The display unit 14 can be a monitor with a display, e.g. acomputer monitor, or the eye tracking system 10 can be implemented as ahead mounted display with an eye tracker. The capturing unit 16 cancomprise one or more cameras capturing the eyes of a user. During acalibration and/or validation procedure of the eye tracking system 10,calibration points and/or validation points can be shown by means of thedisplay unit 14. During the calibration and/or validation and forexample also during subsequent eye tracking, the capturing unit 18captures images of the eyes of a user. These images are then processedby the processing unit 12, which determines according to a predefinedaccuracy function a left accuracy score for the left eye and a rightaccuracy score for the right eye and optionally a binocular accuracyscore for both eyes. Based on these accuracy scores, the processing unit12 determines if one of the left and the right eye of the user isdominant. This result can be outputted on the display unit 14 or to beused for subsequent eye tracking. The illumination unit 18 can compriseone or more light sources, for example infrared LEDs, for illuminatingthe eyes of the user for the purpose of producing glints or otherPurkinje images, which facilitates determining eye properties like thepositions and orientations of eye features, the gazes, and so on.

To sum up, a method operating an eye tracking system and an eye trackingsystem is provided, by means of which advantageously the dominant eye ofa user can be easily determined and which can be used for diagnosticpurposes and especially for achieving much higher accuracy in eyetracking.

The invention claimed is:
 1. A method comprising: capturing a pluralityof images of a first eye of a user and a second eye of the user;determining a plurality of respective points of regard of the first eyefor the plurality of images; determining a first deviation based on anaverage difference between the plurality of respective points of regardof the first eye and a plurality of respective target points;determining a plurality of respective points of regard of the second eyefor the plurality of images; determining a second deviation based on anaverage difference between the plurality of respective points of regardof the second eye and the plurality of respective target points;determining, based on the first deviation and the second deviation, afirst accuracy score for the first eye and a second accuracy score forthe second eye; and determining, based on the first accuracy score andthe second accuracy score, an eye dominance characterization of theuser.
 2. The method of claim 1, wherein determining the eye dominancecharacterization of the user includes determining that the first eye ofthe user is dominant.
 3. The method of claim 1, wherein determining theeye dominance characterization of the user includes determining thatneither the first eye of the user nor the second eye of the user isdominant.
 4. The method of claim 1, wherein determining the eyedominance characterization of the user includes detecting an eyevergence pathology of the user.
 5. The method of claim 1, furthercomprising determining a current point of regard of the user based onthe eye dominance characterization of the user.
 6. The method of claim5, wherein determining the current point of regard of the user based onthe eye dominance characterization of the user includes weighting eyetracking data of the second eye of the user less than eye tracking dataof the first eye of the user.
 7. The method of claim 5, whereindetermining the current point of regard of the user based on the eyedominance characterization of the user includes determining the point ofregard of the user using eye tracking data of the first eye of the userwithout using tracking data of the second eye of the user.
 8. The methodof claim 5, further comprising displaying an element in a graphical userinterface based on the current point of regard.
 9. The method of claim5, further comprising activating an element in a graphical userinterface based on the current point of regard.
 10. The method of claim1, wherein determining the plurality of respective points of regard ofthe first eye for the plurality of images comprises: displaying aplurality of eye tracking indicia at the plurality of respective targetpoints on a display device at a respective plurality of times; anddetermining the plurality of respective points of regard for the firsteye for each of the respective plurality of times.
 11. An apparatuscomprising: at least one camera to capture a plurality of images of afirst eye of a user and a second eye of the user; and at least oneprocessor to: determine a plurality of respective points of regard ofthe first eye for the plurality of images; determine a first deviationbased on an average difference between the plurality of respectivepoints of regard of the first eye and a plurality of respective targetpoints; determine a plurality of respective points of regard of thesecond eye for the plurality of images; determine a second deviationbased on an average difference between the plurality of respectivepoints of regard of the second eye and the plurality of respectivetarget points; determine, based on the first deviation and the seconddeviation, a first accuracy score for the first eye and a secondaccuracy score for the second eye; and determine, based on the firstaccuracy score and the second accuracy score, an eye dominancecharacterization of the user.
 12. The apparatus of claim 11, wherein theat least one processor is to determine the eye dominancecharacterization of the user by detecting an eye vergence pathology ofthe user.
 13. The apparatus of claim 11, wherein the at least oneprocessor is further to determine a current point of regard of the userbased on the eye dominance characterization of the user.
 14. Theapparatus of claim 13, wherein the at least one processor is todetermine the current point of regard of the user based on the eyedominance characterization of the user by weighting eye tracking data ofthe second eye of the user less than eye tracking data of the first eyeof the user.
 15. The apparatus of claim 13, wherein the at least oneprocessor is to determine the current point of regard of the user basedon the eye dominance characterization of the user using eye trackingdata of the first eye of the user without using tracking data of thesecond eye of the user.
 16. The apparatus of claim 13, furthercomprising a display device to present a graphical user interface,wherein the at least one processor is to display or activate an elementin the graphical user interface based on the current point of regard.17. A non-transitory computer-readable medium having instructionsencoded thereon which, when executed by a processing unit of an eyetracker, cause the eye tracker to: capture a plurality of images of afirst eye of a user and a second eye of the user; determining aplurality of respective points of regard of the first eye for theplurality of images; determining a first deviation based on an averagedifference between the plurality of respective points of regard of thefirst eye and a plurality of respective target points; determining aplurality of respective points of regard of the second eye for theplurality of images; determining a second deviation based on an averagedifference between the plurality of respective points of regard of thesecond eye and the plurality of respective target points; determine,based on the first deviation and the second deviation, a first accuracyscore for the first eye and a second accuracy score for the second eye;and determine, based on the first accuracy score and the second accuracyscore, an eye dominance characterization of the user.
 18. Thenon-transitory computer-readable medium of claim 17, wherein theinstructions which, executed by the processing unit, cause the eyetracker to determine the eye dominance characterization of the usercause the eye tracker to detect an eye vergence pathology of the user.19. The non-transitory computer-readable medium of claim 17, wherein theinstructions which, when executed by the processing unit, further causethe eye tracker to: determine a current point of regard of the userbased on the eye dominance characterization of the user; and display oractivate an element in a graphical user interface based on the currentpoint of regard.
 20. The method of claim 1, wherein determining theplurality of respective points of regard of the first eye for theplurality of images includes: determining a plurality of respective rawpoints of regard of the first eye; and mapping the plurality ofrespective raw points of regard of the first eye to a plurality ofrespective calibrated points of regard of the first eye.